trnL outperforms rbcL as a DNA metabarcoding marker when ... - PLOS

RESEARCH ARTICLE

trnL outperforms rbcL as a DNA metabarcoding marker when compared with the observed plant component of the diet of wild white-faced capuchins (Cebus capucinus, Primates) Elizabeth K. Mallott1*, Paul A. Garber2, Ripan S. Malhi2,3

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1 Department of Anthropology, Northwestern University, Evanston, IL, United States of America, 2 Department of Anthropology, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America, 3 Carl R Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, United States of America * [email protected]

OPEN ACCESS Citation: Mallott EK, Garber PA, Malhi RS (2018) trnL outperforms rbcL as a DNA metabarcoding marker when compared with the observed plant component of the diet of wild white-faced capuchins (Cebus capucinus, Primates). PLoS ONE 13(6): e0199556. https://doi.org/10.1371/journal. pone.0199556 Editor: Hideyuki Doi, University of Hyogo, JAPAN Received: January 18, 2018 Accepted: June 8, 2018 Published: June 26, 2018 Copyright: © 2018 Mallott et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: Sequence data is available in the Sequence Read Archive (http:// www.ncbi.nlm.nih.gov/sra) BioProject ID PRJNA299397, and the ecotag output for both the rbcL and trnL sequences can be found in the Supporting Information files.

Abstract DNA metabarcoding is a powerful tool for assessing the diets of wild animals, but there is no clear consensus on which proposed plant barcoding marker is most suitable for dietary analysis. This study compares two DNA plant barcoding markers that are commonly used for dietary analyses from degraded DNA, rbcL and trnL, to detailed dietary observations of wild white-faced capuchins (Cebus capucinus). Observational dietary data and fecal samples (n = 170) were collected for one year from a group of individually recognizable monkeys at La Suerte Biological Field Station, Costa Rica. DNA was extracted and portions of the rbcL and trnL chloroplast were amplified and sequenced on the Illumina MiSeq platform. Sequences were analyzed using obitools. Of the two barcoding markers tested, trnL yielded greater numbers of sequences with equal sequencing effort, higher resolution taxonomic identifications (albeit with a larger reference database), and identified a greater number of families also found in the observed diet. There was no relationship between observed capuchin feeding behavior and dietary composition based on either sequence occurrence or relative abundance of sequences using rbcL as a marker. However, dietary composition based on the relative abundance of trnL sequences was significantly positively associated with the observed percentage of feeding and foraging time capuchins’ spent on each plant species. Additionally, in 35% of cases, the relative abundance of trnL sequences assigned to particular plant families in fecal samples was highly positively correlated with time spent consuming plants from those same families. Our results indicate that trnL is a more robust DNA metabarcoding marker for plant dietary analysis and may potentially be used to quantitatively assess differences in diet within or between species.

Funding: This study was supported with funds from the UIUC Department of Anthropology, the Beckman Institute at UIUC, the Center for Latin American Studies at UIUC, the NSF GRFP program, and the American Philosophical Society (Lewis and

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Clark Fund for Exploration and Field Research) awarded to EKM, and funds awarded to PAG and RSM by the University of Illinois UrbanaChampaign. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

Introduction DNA metabarcoding is quickly becoming a powerful tool in ecology for understanding trophic interactions and non-invasively assessing the diets of wild animals, particularly with the increasing availability of high-throughput sequencing technologies [1,2]. However, DNA barcoding marker genes must be chosen carefully to provide meaningful results [3]. The choice of marker depends on the biology of the study species, the questions being asked, the quality of the DNA sample, and completeness of reference databases [2]. Target regions must be variable enough to discriminate between closely related animal and plant species, yet flanking regions should be conserved enough to be less variable within a single species. Additionally, primer sets should readily amplify a range of species within a large taxonomic group. Finally, the target region must be long enough to yield the desired taxonomic resolution. Even though there is a consensus for some taxonomic groups as to which standardized DNA region should be used for metabarcoding, including dietary analysis (e.g., COI for invertebrates) [4,5], multiple barcoding target regions have been proposed for other taxonomic groups, such as plants [1,3,6,7]. An additional important consideration for dietary analysis using DNA metabarcoding is the ability of a primer to amplify often degraded and low copy number DNA from fecal samples. Mini-barcodes that target regions of DNA that are generally 150bp or smaller allow for the amplification of degraded DNA [8,9]. While these smaller barcoding regions are preferable for degraded samples [3,8,10,11], smaller barcoding regions result in less specific taxonomic identifications [12]. Additionally, in plants, using single locus barcodes can lead to less reliable taxonomic identifications due to high rates of hybridization, introgression, and polyploidy in land plants [13]. Two single-locus regions, trnL and rbcL, have been proposed for dietary analysis using DNA metabarcoding in herbivores and omnivores [1,3,14]. In particular, the trnL-g and trnLh primer set used to amplify the P6 loop of trnL (UAA) [1,15–23] and the rbcLZ1 and rbcL19b mini-barcoding primers used to amplify a small region of rbcL [14,23–25] have been widely used. A few studies have directly compared the performance of rbcL and trnL as barcoding regions [23–26]. Results indicated that trnL had higher amplification success and a larger sequence divergence range in a study of wetland plants [25], while rbcL had a more complete publicly available reference database [26]. Both regions generally were only able to provide taxonomic resolution at the level of genus or family [9]. In addition, rates of sequence divergence, and, therefore, taxonomic resolution of mini-barcodes in these two regions may differ depending on the plant family or families of interest in a particular study [25]. Relatively less is known, however, about how dietary results from each of these barcoding regions compare to other methods of assessing diet, particularly how molecular results compare to fine-scale dietary observations over long time periods. In one study where sika deer (Cervus nippon) were fed a known diet, DNA metabarcoding using trnL was able to reliably detect all of the foods present in the diet when a local reference database was used [20]. However, taxonomic identifications were limited to the genus or family level when using a global database in the same study [20]. Similarly, in a study of red-shanked douc langurs (Pygathrix nemaeus) fed a known diet, DNA metabarcoding using trnL was only able to identify genera known to be in the diet in 31.3–37.5% of cases using a global reference database [27]. Studies comparing microhistological analysis of fecal samples or macroscopic analysis of stomach contents with DNA metabarcoding, using either trnL or rbcL as markers, found varying amounts of overlap between taxa identified by the two methods [15,18,23,28–30]. Many of these studies also showed that DNA metabarcoding provided much greater taxonomic resolution than microhistology [15,18,28,29]. Few studies have compared results from one of the DNA

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Comparison of trnL and rbcL to observed diet

metabarcoding markers with methods of direct observation of diet in a wild mammal. In a study of woodland caribou (Rangifer tarandus caribou), Newmaster et al. (2013) found that results from DNA metabarcoding using trnL and a global reference database and results from animal-borne video recording were highly correlated. Similarly, Hargrave (2015) found that DNA metabarcoding using trnL and a global reference database supplemented with local plant species sequences identified all but one taxon of plants that wild sable antelope (Hippotragus niger) were observed to have consumed. In a study of black and white colobus monkeys (Colobus guereza), Bradley et al. (2007) found that DNA metabarcoding using rbcL and a global reference database identified plants known to be part of an individual’s diet during detailed behavioral observations taken during the previous two days. However, the study failed to detect some plants that an individual was observed eating and identified plants that were not recorded as part of an individual’s diet but may have been consumed [24]. None of these studies have compare the precision of both rbcL and trnL with direct detailed dietary observations collected over more than six months. To fill this gap, here we evaluate the precision and taxonomic resolution of DNA metabarcoding-based dietary analysis using both rbcL and trnL. We compare molecularly-determined diet with detailed individual-level dietary observations of a group of wild white-faced capuchins (Cebus capucinus) over a year-long period. We also assess whether the relative abundance of rbcL or trnL reads is correlated with the percentage of time spent feeding or foraging on food items.

Materials and methods Study site and population The study was conducted from January 2013 through January 2014 at La Suerte Biological Field Station in northeastern Costa Rica (10.445N, 83.784W). La Suerte Biological Field Station includes 170 ha of advanced secondary wet tropical forest and 130 ha of early secondary growth and regenerating pasture [31]. Annual rainfall during the study period was 3116 mm. A group of individually recognizable white-faced capuchins (Cebus capucinus) containing 21– 22 individuals during the study period was observed (a detailed description of the study site and population is found in Mallott et al. 2017) [32]. An overview of sample collection and analysis is found in Fig 1 (Fig 1).

Observational data collection Dietary data were collected during one-hour instantaneous focal animal samples (two-minute interval) of individually recognizable adult males, adult females, and juveniles. Data were collected between 0500 and 1600 hours, and 841 hours of dietary data were collected over 237 days. Activity budget (feeding–actively consuming food items; foraging–manipulating or searching for food items; traveling–movement between or within the crowns of trees which was not for the immediate purpose of obtaining food or engaging in social activity; resting– periods of inactivity; social–engaged in any affiliative or agonistic interaction with at least one other individual) was recorded at each two-minute interval. If an individual was feeding or foraging, diet (ripe fruit, unripe fruit, seeds, flowers, leaves, invertebrates, vertebrates, other) was recorded. Plant food items were identified to species, when possible, and identifications were aided by reference materials in the library at La Suerte Biological Field Station and the knowledge of station staff. Overall, 80% of the plant species consumed by the capuchins were identified to the species level and 86% of plant feeding and foraging records were a known species. Fecal samples were collected from all individuals throughout the observation study period (n = 170) in sterile tubes, fixed in 90% ethanol, and stored at -20C. The mean number of fecal samples collected per month was 13.1±5.3 (range = 4–21). To minimize environmental

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Fig 1. Summary of sample collection and analysis. Flowchart showing sample collection, sample processing for each type of sample, and statistical analyses using the resulting data from each sample type. https://doi.org/10.1371/journal.pone.0199556.g001

contamination, portions of fecal material in direct contact with soil or leaf litter were not collected. CITES export permits were obtained from Sistema Nacional de Areas de Conservacio´n in Costa Rica, and import permits for the United States were obtained from the CDC. Data collection protocols were approved by the University of Illinois IACUC, La Suerte Biological Field Station, the Ministerio de Ambiente, Energı´a, y Telecomunicaciones in Costa Rica, and the Comisio´n Nacional para la Gestio´n de la Biodiversidad in Costa Rica.

Molecular data collection A QIAamp DNA Stool Mini Kit was used to extract DNA from 170 fecal samples, following the provided protocol for “Isolation of DNA from Stool for Human DNA Analysis” (using the

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modifications outlined in Mallott et al. 2015) [33]. A 157bp segment of the rbcL gene was amplified using the rbcLZ1 and rbcL19b primers [14,24], and a 22-143bp segment of the trnL (UAA) intron was amplified using the trnL-g and trnL-h primers [3]. Negative extraction and PCR controls were used to minimize the risk of contamination. Samples were individually indexed, using the same barcode for both rbcL and trnL amplicons for each sample, and the amplicons were amplified on a Fluidigm Access Array system at the Roy J. Carver Biotechnology Center at UIUC (for reaction conditions and detailed methods see Mallott et al. 2017). Amplicons were sequenced on the Illumina MiSeq v2 platform with paired end 2x300 bp reads. In obitools, raw sequences were paired with an alignment score threshold of 20, assigned to samples, de-duplicated, low-count sequences and PCR errors were removed, and the remaining sequences were assigned to taxa using the ecotag script and reference databases for both rbcL and trnL [34]. References databases for rbcL and trnL were created using ecoPCR [35] from release 131 of the European Nucleotide Archive. The rbcL database contained 1589 unique plant sequences which include 92 orders, 284 families, 881 genera, and 1054 species. The trnL database contained 13941 unique plant sequences which include 75 orders, 365 families, 4028 genera, and 8656 species. Tab-delimited sequence read count tables were created in obitools, and the resulting files were collapsed at the operational taxonomic unit, genus, and family levels. Relative abundances were then calculated in the vegan package [36] of R (rproject.org). Details of the bioinformatics analyses can be found in the supporting information (S1 File). Raw sequence data is available in the Sequence Read Archive (http://www.ncbi.nlm. nih.gov/sra), BioProject ID PRJNA299397, and the ecotag output for both the rbcL and trnL sequences can be found in the supporting information (S1 and S2 Tables).

Statistical methods Nonparamentric Mantel tests were used to compare Bray-Curtis dissimilarity matrices of monthly individual observed and molecular diets. Relative abundances of sequences were averaged across the month for individuals for whom we had multiple molecular samples in the same month. To assess relationships between diet analysis methods at the group-level, instead of individual-level, spearman rank correlations were used to test for a positive association between the percentage of the capuchin group’s feeding and foraging time spent consuming plants from a specific family, and the frequency of occurrence of sequences assigned to that family (percentage of fecal samples containing sequences assigned to a family) and average relative abundance of sequences assigned to that same family within a given month for both rbcL and trnL sequencing results.

Results Observed diet During the study period, 47.8% of observed white-faced capuchin feeding and foraging time was spent on fruit, 49.8% on invertebrates, 1.2% on flowers, 0.5% on leaves, 0.01% on vertebrates, and 0.6% on other plant foods. Psidium guajava (Myrtaceae) accounted for 21.2% of feeding and foraging time on fruit items. Hampea appendiculata (Malvaceae) (9.5%), Dipteryx panamensis (Fabaceae) (8.5%), Inga spectabilis (Fabaceae) (6.8%), Urera baccifera (Urticaceae) (6.5%), and Inga thibaudiana (Fabaceae) (6.3%) also accounted for a substantial percentage of white-faced capuchin feeding and foraging time (Table 1). Fifty-nine species of fruit from 27 plant families were consumed during the study period (for detailed dietary results, see S3 Table).

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Table 1. Observed fruit diet based on the percentage of feeding and foraging time spent on each fruit species. Species

Family

Percentage of diet

Average monthly percentage

Months eaten

Psidium guajava

Myrtaceae

21.2%

19.0% (±10.8%)

All

Hampea appendiculata

Malvaceae

9.5%

13.7% (±22.9%)

Jan, Feb, Nov, Dec

Dipteryx panamensis

Fabaceae

8.5%

6.9% (±10.8%)

Jan—Jun

Inga spectabilis

Fabaceae

6.8%

4.5% (±8.6%)

Feb—May, Oct

Urera baccifera

Urticaceae

6.5%

4.1% (±12.6%)

Oct—Dec

Inga thibaudiana

Fabaceae

6.3%

4.6% (±9.6%)

Jan, Mar—May, Jul—Dec

Ficus colubrinae

Moraceae

4.1%

4.0% (±3.8%)

Feb, Mar, May—Aug, Oct—Dec

Ficus trigonata

Moraceae

3.7%

2.5% (±6.8%)

Apr, May, Oct, Nov

Conostegia xalapensis

Melastomataceae

3.4%

2.8% (±3.8%)

Jan, Apr—Jun, Aug, Sep, Nov, Dec

Sapium grandulosum

Euphorbiaceae

2.8%

1.4% (±3.0%)

Jul, Aug, Oct, Nov

Ficus schippii

Moraceae

2.2%

2.6% (±5.6%)

May, Jun, Nov

Inga marginata

Fabaceae

2.1%

2.0% (±3.2%)

Jan, Feb, Jun, Aug, Dec

Ficus tonduzii

Moraceae

1.8%

1.2% (±3.1%)

May, Oct

Nephelium lappaceum

Sapindaceae

1.7%

1.7% (±3.0%)

Jun—Oct

Cestrum megalophylum

Solanaceae

1.7%

1.1% (±4.1%)

Dec

Dendropanax arboreus

Araliaceae

1.6%

1.6% (±5.4%)

Sep, Oct

Piper spp.

Piperaceae

1.3%

1.0% (±1.1%)

Feb, Apr, Nov, Dec

Miconia affinis

Melastomataceae

1.2%

1.1% (±2.3%)

Apr—Jun, Aug, Sep

Casearia arborea

Salicaceae

1.1%

1.1% (±2.3%)

Jun, Oct, Nov

Miconia elata

Melastomataceae

1.1%

0.5% (±0.0%)

Apr, May, Aug, Nov

https://doi.org/10.1371/journal.pone.0199556.t001

Molecular dietary analysis results Sequencing of rbcL amplicons yielded 1706 unique sequences, after dereplicating and denoising the sequence dataset. Thirty-six families, 39 genera, and 36 species were identified using rbcL (Table 2 and S4 Table). Sequences assigned to Solanaceae were the most frequently occurring rbcL sequences in the fecal samples, occurring in 99.4% of samples. Sequences assigned to Melastomataceae (97.1%), Araceae (88.2%), Moraceae (78.8%), Myrtaceae (66.5%), Urticaceae (47.7%), Fabaceae (41.8%), and Convolvulaceae (40.0%) also were among the most frequently occurring rbcL sequences (Table 2). These families also accounted for the highest relative abundances of rbcL sequences, with Solanaceae accounting for 62.9% of sequences assigned to family, Melastomataceae for 17.7%, Araceae for 8.0%, Moraceae for 4.0%, Myrtaceae for 2.6%, and Urticaceae for 1.5%. Sequencing of trnL amplicons yielded 4551 unique sequences. Forty-five families, 68 genera, and 69 species were identified using trnL (Table 2 and S5 Table). Sequences assigned to Myrtaceae were the most frequently occurring trnL sequences in the fecal samples, occurring in 100% of samples. Sequences assigned to Fabaceae (99.4%), Musaceae (98.8%), Solanaceae (97.7%), Piperaceae (90.0%), Malvaceae (89.4%), Urticaceae (85.9%), Euphorbiaceae (83.5%), and Melastomataceae (80.0%) were among the most frequently occurring trnL sequences (Table 2). Many of these families also had high relative abundances of trnL sequences, with Myrtaceae accounting for 50% of sequences assigned to family, Solanaceae for 16.4%, Malvaceae for 7.1%, Piperaceae for 3.3%, and Urticaceae for 3.1%. Overall, sequencing of trnL amplicons resulted in 311% more sequences and 267% more unique sequences, likely partially due to the shorter length of the trnL amplicon. A higher percentage of trnL sequences were identified to both order and family, a higher percentage of observed families also were identified with trnL, and the trnL method resulted in a greater number of identified families, genera, and species when compared with rbcL (Table 3). This

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Table 2. Percentage of observed feeding and foraging records assigned to each family of fruit and frequency of occurrence and relative abundance of rbcL and trnL sequences over the course of the entire study period. Family

Percentage of Frequency of observed fruit occurrence of feeding and foraging rbcL sequences records

Average relative Total relative Frequency of Average relative Total relative abundance of rbcL abundance of rbcL occurrence of trnL abundance of trnL abundance of trnL sequences sequences sequences sequences sequences

Acanthaceae

-

-

-

-

11.76%

0.02%

0.01%

Annonaceae

-

-

-

-

74.71%

2.16%

1.93%

Apiaceae

-

1.18%

0.01%

0.00%

-

-

-

0.20%

-

-

-

-

-

-

-

88.24%

11.26%

7.99%

-

-

-

Araliaceae

1.58%

-

-

-

-

-

-

Arecaceae

0.78%

-

-

-

33.53%

0.62%

0.76%

Apocynaceae Araceae

Asteraceae

-

-

-

-

0.59%

0.00%

0.00%

Bignoniaceae

0.32%

12.35%

1.08%

0.31%

31.76%

0.22%

0.26%

Boraginaceae

0.09%

1.76%

0.01%

0.00%

-

-

-

Brassicaceae

-

0.59%

0.00%

0.00%

-

-

-

Bromeliaceae

-

-

-

-

60.00%

1.64%

2.76% 0.13%

Burseraceae

-

-

-

-

18.82%

0.17%

Calyceraceae

-

19.41%

0.96%

0.85%

-

-

-

Canellaceae

-

-

-

-

3.53%

0.00%

0.00%

Capparaceae

-

-

-

-

1.76%

0.01%

0.01%

Caprifoliaceae

-

1.18%

0.06%

0.01%

-

-

-

Caricaceae

-

-

-

-

28.24%

0.66%

2.33%

Chenopodiaceae

-

1.76%

0.00%

0.00%

-

-

-

Chrysobalanaceae

-

-

-

-

2.94%

0.01%

0.00%

Clusiaceae

0.11%

17.65%

2.09%

0.44%

24.12%

0.63%

0.01%

Convolvulaceae

0.06%

40.00%

0.24%

0.11%

18.24%

0.01%

0.00%

Cordiaceae

-

-

-

-

10.00%

0.02%

0.00%

Costaceae

-

0.59%

0.04%

0.00%

7.06%

0.13%

0.00%

Cucurbitaceae

-

8.82%

0.01%

0.00%

-

-

-

Cyclanthaceae

-

2.94%

0.01%

0.02%

-

-

-

Euphorbiaceae

2.90%

4.12%

0.22%

0.11%

83.53%

2.66%

2.03%

Fabaceae

25.09%

41.76%

4.37%

0.40%

99.41%

5.65%

2.13%

Gesneriaceae

-

2.35%

0.07%

0.02%

2.35%

0.01%

0.01%

Heliconiaceae

0.34%

-

-

-

-

-

-

Hypoxidaceae

-

-

-

-

2.35%

0.00%

0.00%

Iteaceae

-

4.71%

0.01%

0.00%

-

-

-

Lamiaceae

0.43%

17.65%

0.90%

0.47%

25.29%

0.58%

0.09%

Lauraceae

0.06%

2.35%

0.01%

0.01%

24.71%

0.12%

0.18%

-

21.18%

0.41%

0.00%

27.65%

0.05%

0.00%

Lecythidaceae Malpighiaceae

0.06%

-

-

-

-

-

-

Malvaceae

10.05%

2.35%

0.18%

0.01%

89.41%

5.52%

7.08%

Marantaceae

-

-

-

-

2.35%

0.06%

0.00%

Melastomataceae

6.17%

97.06%

15.11%

17.73%

80.00%

2.17%

1.73%

Moraceae

16.19%

78.82%

10.14%

4.01%

60.00%

3.42%

0.72%

Musaceae

0.17%

-

-

-

98.82%

3.48%

2.55%

Myristicaceae

0.17%

-

-

-

0.59%

0.01%

0.00%

Myrtaceae

21.27%

66.47%

9.64%

2.64%

100.00%

40.69%

49.90%

Onagraceae

-

2.94%

0.00%

0.00%

-

-

(Continued)

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Table 2. (Continued) Family

Percentage of Frequency of observed fruit occurrence of feeding and foraging rbcL sequences records

Average relative Total relative Frequency of Average relative Total relative abundance of rbcL abundance of rbcL occurrence of trnL abundance of trnL abundance of trnL sequences sequences sequences sequences sequences

Orchidaceae

-

2.35%

0.04%

0.00%

-

-

-

Orobanchaceae

-

0.59%

0.00%

0.00%

0.59%

0.00%

0.00%

Papaveraceae

-

7.06%

0.03%

0.00%

-

-

-

Passifloraceae

-

-

-

-

24.71%

0.08%

0.09%

Piperaceae

1.29%

-

-

-

90.00%

4.33%

3.31%

Platanaceae

-

-

-

-

23.53%

0.16%

0.02%

Poaceae

-

-

-

-

7.06%

0.09%

0.02%

Primulaceae

-

-

-

-

1.18%

0.00%

0.00%

Rhamnaceae

-

-

-

-

24.71%

1.28%

0.10%

Rosaceae

-

2.35%

0.00%

0.00%

-

-

-

Rubiaceae

1.12%

11.18%

0.67%

0.11%

47.06%

1.29%

1.26%

Salicaceae

1.15%

-

-

-

-

-

-

Sapindaceae

1.72%

-

-

-

58.24%

0.74%

0.68%

Sapotaceae

0.26%

-

-

-

22.94%

0.14%

0.28%

Simaroubaceae

0.17%

-

-

-

2.35%

0.20%

0.02%

Solanaceae

1.69%

99.41%

36.61%

62.90%

97.65%

15.24%

16.39%

Strelitziaceae

-

0.59%

0.00%

0.00%

-

-

-

Trochodendraceae

-

2.35%

0.12%

0.00%

-

-

-

Typhaceae

-

-

-

-

4.12%

0.00%

0.00%

Ulmaceae

-

5.29%

0.00%

0.00%

-

-

-

Urticaceae

6.55%

47.65%

4.97%

1.53%

85.88%

5.66%

3.06%

Velloziaceae

-

-

-

-

0.59%

0.00%

0.00%

Zingiberaceae

-

16.47%

0.75%

0.33%

20.59%

0.02%

0.12%

Average relative abundances are calculated across all fecal samples collected during the study period. https://doi.org/10.1371/journal.pone.0199556.t002

result is not surprising, however, given the larger size of the trnL reference database used for this study. A greater percentage of rbcL sequences were identified at the levels of genus and Table 3. Comparison of rbcL and trnL sequencing performance, resolution, and accuracy. rbcL Sequences Unique sequences

trnL 2,541,027

7,910,288

1706

4551

Percentage identified to order

53.8%

89.7%

Percentage identified to family

47.2%

79.2%

Percentage identified to genus

9.1%

8.2%

Percentage identified to species

7.7%

2.0%

Identified families also in observed diet

14

21

Total number of families identified

67

Families identified with all three methods

14

Families identified with both rbcL and trnL

19

Families only identified with observational data

5

Families only identified with rbcL

16

Families only identified with trnL

20

https://doi.org/10.1371/journal.pone.0199556.t003

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Comparison of trnL and rbcL to observed diet

species (Table 3), which is surprising as the trnL database contains four times as many genera and eight times as many species as the rbcL database. Of the 67 total families identified in this study, 14 were detected with all three methods and 19 were detected with both rbcL and trnL (Table 3). While the uneven distribution of fecal samples across months likely contributed to the fact that neither rbcL or trnL detected all species of food present in the observational diet, 92.5% of plant families found in the observation diet were also found using the DNA metabarcoding methodology (Table 3).

Comparison of observed and molecularly determined diet

0.2

0.4

0.6

0.0

0.1

0.2

0.3

0.80

0.90

1.00

Piperaceae

0.70

0.5

0.6

0.7

0.8

0.9

Myrtaceae

Frequency of occurence of trnL sequences

0.0

Frequency of occurence of rbcL sequences

0.0

0.2

0.4

Malvaceae

−0.2

Frequency of occurence of rbcL sequences

Non-parametric Mantel tests showed a significant positive correlation between the observed dietary composition, based on feeding and foraging time in a given month, and dietary composition for each month determined with trnL (ρ = 0.1025, p = 0.045). These results indicate that individuals with more similar monthly diets assessed with DNA metabarcoding using trnL had more similar monthly diets based on feeding and foraging observations. However, the same was not true for rbcL. There was no relationship between observed monthly plant dietary composition and monthly dietary composition determined with rbcL (ρ = 0.0653, p = 0.145). There was no relationship between the frequency of occurrence of either rbcL or trnL sequences assigned to a family in fecal samples in a given month and the percentage of feeding and foraging time the group was observed to be eating food items from that same family in that month (S6 Table). The exceptions to this were the significant positive correlations between the frequency of occurrence of rbcL sequences assigned to Myrtaceae and percentage of feeding and foraging time spent consuming Myrtaceae (ρ = 0.4869, p = 0.046), and the frequency of occurrence of trnL sequences assigned to Piperaceae and percentage of feeding and foraging time spent consuming Piperaceae (ρ = 0.6763, p = 0.006) (Fig 2). There were several plant families for which the average relative abundance of rbcL or trnL sequences were significantly positively correlated with the percentage of feeding and foraging time the group spent consuming that same family in a given month (S6 Table). The average

−0.10

0.00

0.10

Observed percentage of feeding and foraging time Fig 2. Relationship between feeding and foraging time and frequency of occurrence of rbcL and trnL sequences. Correlations between observed percentage of group feeding and foraging time spent on each plant family in a given month and the frequency of occurrence of rbcL or trnL sequences in the same month. https://doi.org/10.1371/journal.pone.0199556.g002

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Comparison of trnL and rbcL to observed diet

relative abundance of rbcL sequences assigned to Fabaceae, Malvaceae, Myrtaceae, and Rubiaceae were significantly positively correlated with the percentage of monthly feeding and foraging time spent exploiting those same families (ρ = 0.7473, p = 0.002; ρ = 0.5077, p = 0.038; ρ = 0.5840, p = 0.016; ρ = 0.5840, p = 0.018) (Fig 3 and S6 Table). The average relative abundance of trnL sequences assigned to Malvaceae, Melastomataceae, Moraceae, Myrtaceae, Piperaceae, Rubiaceae, and Simaroubaceae were significantly positively correlated with the percentage of feeding and foraging time spent on those same families (ρ = 0.8118, p